Book Image

Data Ingestion with Python Cookbook

By : Gláucia Esppenchutz
Book Image

Data Ingestion with Python Cookbook

By: Gláucia Esppenchutz

Overview of this book

Data Ingestion with Python Cookbook offers a practical approach to designing and implementing data ingestion pipelines. It presents real-world examples with the most widely recognized open source tools on the market to answer commonly asked questions and overcome challenges. You’ll be introduced to designing and working with or without data schemas, as well as creating monitored pipelines with Airflow and data observability principles, all while following industry best practices. The book also addresses challenges associated with reading different data sources and data formats. As you progress through the book, you’ll gain a broader understanding of error logging best practices, troubleshooting techniques, data orchestration, monitoring, and storing logs for further consultation. By the end of the book, you’ll have a fully automated set that enables you to start ingesting and monitoring your data pipeline effortlessly, facilitating seamless integration with subsequent stages of the ETL process.
Table of Contents (17 chapters)
1
Part 1: Fundamentals of Data Ingestion
9
Part 2: Structuring the Ingestion Pipeline

Ingesting Parquet files

Apache Parquet is a columnar storage format that is open source and designed to support fast processing. It is available to any project in a Hadoop ecosystem and can be read in different programming languages.

Due to its compression and fastness, this is one of the most used formats when needing to analyze data in great volume. The objective of this recipe is to understand how to read a collection of Parquet files using PySpark in a real-world scenario.

Getting ready

For this recipe, we will need SparkSession to be initialized. You can use the code provided at the beginning of this chapter to do so.

The dataset for this recipe will be Yellow Taxi Trip Records from New York. You can download it by accessing the NYC Government website and selecting 2022 | January | Yellow Taxi Trip Records or using this link:

https://d37ci6vzurychx.cloudfront.net/trip-data/yellow_tripdata_2022-01.parquet

Feel free to execute the code with a Jupyter notebook...